Automatic machine translation engines may be designed for general purpose translation of whole sentences, making use of linguistic context to perform translations. Examples of such applications include Systran®, Language Weaver®, Apptek®, LogoMedia/LEC®, and Google Translate®. However, with respect to structured data found in databases and spreadsheets, or semi-structured data that is extracted from full text via an information extraction engine, frequent translation errors occur due to a lack of knowledge of a category for noun phrases (“NPs”) for the structured or semi-structured information. For example, the Russian names “Sergey Lozhechko” and “Shedrov Aleksandr” should be left alone, or transliterated. Transliteration involves changing characters of one language into corresponding characters of another language. For instance, the Greek “X” may be transliterated into English as “ch”. However, these names could instead be literally translated as “Sergey spoon” and “generous Alexander”, respectively, without an understanding by the system that it is dealing with names of people. Similarly, the Russian word “” means “street” in the context of an address, but means “brochure” or “prospectus” in the context of a document type. Such errors due to a lack of linguistic or natural language context that is present in higher level units of linguistic analysis, such as sentences, paragraphs and full text documents, may render conventional automatic machine translation engines inaccurate for translating noun phrases in structured or semi-structured data.